Pavlovian summation: Data and theory.

Ghirlanda, Stefano

doi:10.1037/xan0000265

Cited by 1 publication

(2 citation statements)

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“…If a maximum M can be identified for the CR, then Equation 1 can be modified as follows: which can be interpreted as the expected magnitude of the CR. This equation fits well summation data on autoshaped magazine entries and lever presses in rats, as well as human data using arbitrary rating scales (Ghirlanda, 2021).…”

Section: Discussionsupporting

confidence: 68%

“…Given enough data, it is possible to test such relationships as we tested Equation 2 and Equation 10 above. Further, we could estimate the values of g A , AB and g B , AB by fitting the model to empirical data, thus testing different theories of generalization (Ghirlanda, 2021). Ideally, data would be collected throughout the learning process to span the full range of CR probabilities, such as in Figure 2.…”

Section: The General Associative Modelmentioning

confidence: 99%

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A response function that maps associative strengths to probabilities.

Ghirlanda¹

2022

Journal of Experimental Psychology: Animal Learning and Cogniti

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Bridging associative and normative theories of animal learning, I show that an associative system can behave as if performing probabilistic inference by using the function f(V) = 1 − e−cV to transform associative strengths (V) into response probabilities. For example, using this function, an associative system can respond normatively to a compound stimulus AB, given previous separate experiences with the components A and B. The CR probability formulae that result from the proposed function have a normative interpretation in terms of statistical decision theory. The formulae also suggest a normative interpretation of stimulus generalization as a heuristic to infer whether different stimuli are likely to convey redundant or independent information about reinforcement.

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Section: Discussionsupporting

confidence: 68%

Section: The General Associative Modelmentioning

confidence: 99%